cognitive constraints in students’ performances

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Cognitive Constraints Cognitive Constraints in Students’ in Students’ Performance: Analysis Performance: Analysis and Tentative Solution and Tentative Solution Dr Sanjoy Sanyal Dr Sanjoy Sanyal Associate Professor Associate Professor Seychelles Seychelles 2006 2006 Presented at a staff seminar in a medical college in Seychelles in 2006

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Describes aspects of students' cognition, Learning styles and cognitive overload

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Page 1: Cognitive Constraints In Students’ Performances

Cognitive Constraints in Cognitive Constraints in Students’ Performance: Students’ Performance: Analysis and Tentative Analysis and Tentative

SolutionSolution

Dr Sanjoy SanyalDr Sanjoy Sanyal

Associate ProfessorAssociate Professor

SeychellesSeychelles

20062006Presented at a staff seminar in a medical college in Seychelles in 2006

Page 2: Cognitive Constraints In Students’ Performances

ProblemProblem

Acceptance of problemAcceptance of problem Identification of further problemIdentification of further problem Assessment of problemAssessment of problem Solution to problemSolution to problem

Page 3: Cognitive Constraints In Students’ Performances

Identification and AssessmentIdentification and Assessment

Information loadInformation load Information overloadInformation overload Cognitive loadCognitive load MemoryMemory Learning curveLearning curve Learning styles/preferences Learning styles/preferences Brain paradigmsBrain paradigms Psychological personality typesPsychological personality types

Page 4: Cognitive Constraints In Students’ Performances

Temporal considerationsTemporal considerations

Theoretically 16 weeksTheoretically 16 weeks Deduct ESExam and Prep weeksDeduct ESExam and Prep weeks Available 14 weeksAvailable 14 weeks Available 98 daysAvailable 98 days

• 14 Saturdays & 14 Sundays14 Saturdays & 14 Sundays• 7070 weekdays weekdays

Deduct 1 weekday off each weekDeduct 1 weekday off each week Available 56 weekdaysAvailable 56 weekdays

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Temporal considerations Temporal considerations Consider:Consider:

• 5 hours on weekdays5 hours on weekdays• 8 hours on Saturdays8 hours on Saturdays• 10 hours on Sundays10 hours on Sundays

5 h x 56 weekdays = 280 weekday-hrs5 h x 56 weekdays = 280 weekday-hrs 8 h x 14 Saturdays = 112 Saturday-hrs8 h x 14 Saturdays = 112 Saturday-hrs 10 h x 14 Sundays = 140 Sunday-hrs10 h x 14 Sundays = 140 Sunday-hrs Total = Total = 532 hours532 hours

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Textual magnitudeTextual magnitude

AnatomyAnatomy = 1155 pages = 1155 pages• Less 10% = 1155 – 115 =Less 10% = 1155 – 115 = 10401040 pg pg

HistologyHistology = 407 pages = 407 pages• Less 10% = 407 – 41 =Less 10% = 407 – 41 = 366366 pages pages

EmbryologyEmbryology = 348 = 348• Less 10% = 348 – 35 =Less 10% = 348 – 35 = 313313 pages pages

Medical TerminologyMedical Terminology = 922 pages = 922 pages• Less 10% = 922 – 92 =Less 10% = 922 – 92 = 830830 pages pages

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Information loadInformation load

AnatomyAnatomy: 1040/532 = 1.95 pg/hour: 1040/532 = 1.95 pg/hour HistologyHistology: 366/532 = 0.69 pg/hr: 366/532 = 0.69 pg/hr EmbryologyEmbryology: 313/532 = 0.59 pg/hr: 313/532 = 0.59 pg/hr Med TermMed Term: 830/532 = 1.56 pg/hr: 830/532 = 1.56 pg/hr

TOTAL ~ TOTAL ~ 5 pages / hour5 pages / hour

Page 8: Cognitive Constraints In Students’ Performances

Information loadInformation load

PhysiologyPhysiology: 1.37 pages/hour: 1.37 pages/hour BiochemistryBiochemistry: 1.35 pages/hour: 1.35 pages/hour NeurosciencesNeurosciences: 0.87 page/hour: 0.87 page/hour

TOTAL ~ TOTAL ~ 4 pages / hour4 pages / hour

Page 9: Cognitive Constraints In Students’ Performances

Information overloadInformation overload

From Rudolph Hanka, University of Cambridge

1014 synapses

Connectivity formula

n(n-1)/2

Skull capacity = 400 cc to 1400 cc over 5 mill years

But no change over several thousand years

Doubling x 3 mill yrsDoubling x 1.5 mill y

rs

1011 neurons

5 x 1021 synapses

Page 10: Cognitive Constraints In Students’ Performances

Information overloadInformation overload

From Rudolph Hanka, University of Cambridge

Logarithmic scale

Information doubling every 33 years1st century 20→21→22→23

2nd century →24→ 25 →26

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Information overloadInformation overload

From Rudolph Hanka, University of Cambridge

No change in

Information doubling every 33 years

over several 1000 years

Page 12: Cognitive Constraints In Students’ Performances

500 years ago500 years ago ArchitectArchitect AnatomistAnatomist SculptorSculptor EngineerEngineer InventorInventor GeometerGeometer ScientistScientist MathematicianMathematician Musician Musician Painter Painter

Leonardo da Vinci

Page 13: Cognitive Constraints In Students’ Performances

200 years ago200 years ago

AnatomistAnatomist PhysicianPhysician SurgeonSurgeon GynecologistGynecologist ObstetricianObstetrician PediatricianPediatrician OrthopedicianOrthopedician

Andreas Vesalius

Page 14: Cognitive Constraints In Students’ Performances

Depth vs. width trade-offDepth vs. width trade-off

IC

ICIC

IC

IC

ICIC IC

“We are learning more and more of less and less;

One day we would know everything about nothing”

Leo

Ves

Now

Page 15: Cognitive Constraints In Students’ Performances

Cognitive loadCognitive load BrachiocephalicBrachiocephalic

PhrenicopleuralPhrenicopleural

Vasa breviaVasa brevia

MusculophrenicMusculophrenic

Internal jugularInternal jugular

Lateral funiculusLateral funiculus

SpinothalamicSpinothalamic

CuneocerebellarCuneocerebellar

Tractus solitariusTractus solitarius

Funiculus gracilisFuniculus gracilis

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Cognitive loadCognitive load Gostak distims Gostak distims

doshesdoshes

BoetimperoferousBoetimperoferous

IsenorinopicllesIsenorinopiclles

HalptemiloginousHalptemiloginous

CzashmigedonixCzashmigedonix

EtrigenxousEtrigenxous

Acondolytic effectAcondolytic effect

FekritionoxesFekritionoxes

DonglistonixDonglistonix

GrodoscinamityGrodoscinamity

Page 17: Cognitive Constraints In Students’ Performances

Cognitive loadCognitive load

Adapted from Odell, King’s College, London

Page 18: Cognitive Constraints In Students’ Performances

Cognitive loadCognitive load

Adapted from Odell, King’s College, London

Page 19: Cognitive Constraints In Students’ Performances

Cognitive loadCognitive load

Adapted from Odell, King’s College, London

Page 20: Cognitive Constraints In Students’ Performances

Cognitive loadCognitive load

Adapted from Odell, King’s College, London

Page 21: Cognitive Constraints In Students’ Performances

Cognitive loadCognitive load

Adapted from Odell, King’s College, London

Page 22: Cognitive Constraints In Students’ Performances

MemoryMemory

Adapted from Dix & Finlay

Page 23: Cognitive Constraints In Students’ Performances

MemoryMemory

Adapted from Dix & Finlay

Page 24: Cognitive Constraints In Students’ Performances

Learning CurveLearning Curve ““Practice makes perfectPractice makes perfect"" a.k.a. Progress Functions a.k.a. Progress Functions Applied to all types of workApplied to all types of work

• EffortEffort decreases by constant % each time decreases by constant % each time output quantity is doubledoutput quantity is doubled

• More times a task is performed, less More times a task is performed, less timetime is is required on each subsequent iterationrequired on each subsequent iteration

Steep learning curve = something gets Steep learning curve = something gets easier quicklyeasier quickly

Page 25: Cognitive Constraints In Students’ Performances

Learning curveLearning curve

Adapted from Wikipedia

Exponential decayPlot shows decay for decay constants of 25, 5, 1, 1/5,

and 1/25. Large decay constants make the quantity vanish almost immediately; smaller decay constants lead to almost-imperceptible decrease.

Rate of decrease of ‘time taken’ or ‘effort required’ with each iteration

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Learning stylesLearning styles

Everybody has a Learning Style Everybody has a Learning Style (Preference)(Preference)

No single type is wrong/right; No single type is wrong/right; good/badgood/bad

Related to cognitive and mental Related to cognitive and mental abilitiesabilities

71 models of learning styles71 models of learning styles

Page 27: Cognitive Constraints In Students’ Performances

Learning styles and cognitionLearning styles and cognition

Adapted from JISC, UKAdapted from Bendigo Senior Secondary College, Aus

Page 28: Cognitive Constraints In Students’ Performances

Learning style modelsLearning style models

Felder-SolomanFelder-Soloman: : AR-SI-VV-SGAR-SI-VV-SG

Honey-MumfordHoney-Mumford: : ARTPARTP

KolbKolb: ADAC / DW-: ADAC / DW-TF /CE-RO-AC-AETF /CE-RO-AC-AE

Page 29: Cognitive Constraints In Students’ Performances

Learning style modelsLearning style models

FlemingFleming: VARK-Multi: VARK-Multi

Bandler, GrinderBandler, Grinder (NLP): VAK(Ad)(NLP): VAK(Ad)

MartinezMartinez (LO): TPCR (LO): TPCR

MemleticsMemletics: VAK-VLSS: VAK-VLSS

Adapted from Memletics

Page 30: Cognitive Constraints In Students’ Performances

Brain paradigmsBrain paradigms

Each style uses different parts of Each style uses different parts of brainbrain

Paul MacLeanPaul MacLean: Triune brain : Triune brain modelmodel

Page 31: Cognitive Constraints In Students’ Performances

Brain paradigmsBrain paradigms

Roger Sperry / Rose & NichollRoger Sperry / Rose & Nicholl: : Right-left brain modelRight-left brain model

Page 32: Cognitive Constraints In Students’ Performances

Brain paradigmsBrain paradigms

Ned Herrmann: Ned Herrmann: 4 (whole)-brain model4 (whole)-brain model

Page 33: Cognitive Constraints In Students’ Performances

Personality TypesPersonality Types Isabel Briggs Isabel Briggs

MeyersMeyers: MBTI: MBTI 4 scales: EI-SN 4 scales: EI-SN

-TF-JP-TF-JP 224 = 4 = 16 types16 types

Guardian Artisan

RationalistIdealist

Page 34: Cognitive Constraints In Students’ Performances

Personality TypesPersonality Types

David David KeirseyKeirsey: GARI : GARI Temperament Temperament sortersorter

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Multiple IntelligenceMultiple Intelligence

Howard GardnerHoward Gardner / / Gary HarmsGary Harms Multiple (7 + 1) Multiple (7 + 1) intelligence – intelligence – related to related to Memletics learning Memletics learning styles (VAK-VLSS + styles (VAK-VLSS + Naturalistic)Naturalistic)

Page 36: Cognitive Constraints In Students’ Performances

Cognitive PsychologyCognitive Psychology Guilbert domainsGuilbert domains::

CognitiveCognitive PsychomotoPsychomotorr

AffectiveAffective

KnowledgKnowledgee

SkillsSkills AttitudeAttitude•Bloom’s taxonomyBloom’s taxonomy: :

•KnowledgeKnowledge•ComprehensionComprehension•ApplicationApplication•AnalysisAnalysis•SynthesisSynthesis•EvaluationEvaluation

Page 37: Cognitive Constraints In Students’ Performances

Tentative SolutionTentative Solution Conduct surveyConduct survey

Identify: Identify:

• Intelligence preference-based LS Intelligence preference-based LS • Personality-based LSPersonality-based LS• Dominant tDominant thinking patternhinking pattern• Stress levelStress level

Page 38: Cognitive Constraints In Students’ Performances

Tentative Solution Tentative Solution Once students knowOnce students know what learning what learning

type they are, they can capitalize on type they are, they can capitalize on their strengths and strengthen their their strengths and strengthen their weaknessesweaknesses

Once we knowOnce we know what type they are, what type they are, we can plan the foundation for we can plan the foundation for appropriately-oriented course appropriately-oriented course deliverydelivery

Page 39: Cognitive Constraints In Students’ Performances

ConclusionConclusion

Adapted from Bendigo SS College, Australia

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ReferencesReferences Hanka R. Information overload and Hanka R. Information overload and

'just-in-time' knowledge. University of 'just-in-time' knowledge. University of Cambridge, 1997. Cambridge, 1997. http://www.medinfo.cam.ac.uk/miu/paphttp://www.medinfo.cam.ac.uk/miu/papers/hanka/mic97/just_in_time.htmlers/hanka/mic97/just_in_time.html

Odell E. Assessment by e-Learning Odell E. Assessment by e-Learning http://www.kcl.ac.uk/content/1/c4/49/01http://www.kcl.ac.uk/content/1/c4/49/01/assessment%20e-learning%20Odell.pp/assessment%20e-learning%20Odell.pptt

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ReferencesReferences Dix A, Finlay J, Abowd GD, Beale R. Dix A, Finlay J, Abowd GD, Beale R.

Human-Computer Interaction (3rd Human-Computer Interaction (3rd edition, 2003) edition, 2003) http://www.hcibook.com/e3/http://www.hcibook.com/e3/

Learning Curve Calculator. Learning Curve Calculator. http://www1.jsc.nasa.gov/bu2/learn.htmhttp://www1.jsc.nasa.gov/bu2/learn.htmll

Experience curve effects Experience curve effects http://http://en.wikipedia.org/wiki/Learning_curveen.wikipedia.org/wiki/Learning_curve

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ReferencesReferences Exponential decay Exponential decay

http://en.wikipedia.org/wiki/Exponential_http://en.wikipedia.org/wiki/Exponential_decaydecay

Joint Information Systems Committee. Joint Information Systems Committee. http://www.jisc.ac.uk/uploaded_documehttp://www.jisc.ac.uk/uploaded_documents/Stage%202%20Learning%20Stylesnts/Stage%202%20Learning%20Styles%20(Version%201).pdf%20(Version%201).pdf

Bendigo Senior Secondary College. Bendigo Senior Secondary College. http://www.bssc.edu.au/public/learning_thttp://www.bssc.edu.au/public/learning_teaching/pd/toc/files/hbdt.ppt eaching/pd/toc/files/hbdt.ppt

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ReferencesReferences Learning Disabilities Resource Learning Disabilities Resource

Community: Community: http://www.ldrc.ca/projects/miinventoryhttp://www.ldrc.ca/projects/miinventory/mitest.html /mitest.html

O'Connor T. Indiana State University. O'Connor T. Indiana State University. 1997 February 21. Using Learning 1997 February 21. Using Learning Styles to Adapt Technology for Higher Styles to Adapt Technology for Higher Education Education http://web.indstate.edu/ctl/styles/learnihttp://web.indstate.edu/ctl/styles/learning.htmlng.html